Decision support for disease characterization and treatment response with disease and peri-disease radiomics

ABSTRACT

Methods, apparatus, and other embodiments associated with classifying a region of tissue using textural analysis are described. One example apparatus includes an image acquisition logic that acquires an image of a region of tissue demonstrating cancerous pathology, a delineation logic that distinguishes nodule tissue within the image from the background of the image, a perinodular zone logic that defines a perinodular zone based on the nodule, a feature extraction logic that extracts a set of features from the image, a probability logic that computes a probability that the nodule is benign or that the nodule will respond to a treatment, and a classification logic that classifies the nodule tissue based, at least in part, on the set of features or the probability. A prognosis or treatment plan may be provided based on the classification of the image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application62/201,837 filed Aug. 6, 2015.

BACKGROUND

Variations of nodule invasiveness and morphology relate to prognosis andpatient outcomes. One approach for diagnosing disease ishistopathological examination of biopsy tissue. The examination mayproduce a diagnostic profile based on attributes including cellmorphology, cytoplasmic changes, cell density, or cell distribution.Visual characterization of tumor morphology is, however, time consuming,and expensive. Visual characterization is also subjective and thussuffers from inter-rater and intra-rater variability. Conventionalvisual characterization of nodule morphology by a human pathologist maytherefore be less than optimal in clinical situations where timely andaccurate classification can affect patient outcomes.

Computed tomography (CT) is frequently used to image nodules. Forexample, chest CT imagery may be used to detect and diagnose non-smallcell lung cancer. However, conventional approaches to analyzing chest CTimagery have been challenged when attempting to distinguish a benigngranuloma (Gr) from malignant adenocarcinoma (AC). For example,conventional CT-based approaches may find it difficult, if even possibleat all, to reliably discriminate nodules caused by benign fungalinfections from non-small cell lung cancer nodules. Histoplasmosis is acommon endemic fungal infection in the United States. Granulomassecondary to histoplasmosis infection may appear identical to malignantlung nodules in CT imagery.

Other cancer types pose challenges when determining treatments orpredicting response to treatment. Magnetic resonance imaging (MRI) is acommon medical imaging modality for preparing or analyzing neo-adjuvantchemotherapy (NAC) for breast cancer. Administered prior to surgery, NACcan reduce the extent of tumor burden and increase a patient's surgicaloptions. The ideal outcome of NAC is pathological complete response(pCR), which is the complete disappearance of residual invasive tumorcells within excised breast tissue. However, less than 25% of breastcancer patients who undergo NAC will achieve pCR.

Since radiologists may be challenged to reliably distinguish Grsecondary to benign fungal infections from AC in situ using conventionalCT approaches in clinically optimal or relevant time frames, invasiveprocedures may be performed that ultimately result in a negativediagnosis. For example, many patients with benign granulomas aresubjected to unnecessary surgical resections and biopsies. Theseinvasive procedures take time, cost money, and put a patient atadditional risk. As the number of routine chest CT scans increases withthe wide-spread adoption of CT-based lung cancer screening protocols, itwould be beneficial to reduce unnecessary thoracotomies, bronchoscopies,biopsies, and other invasive procedures. Similarly, breast cancerpatients would benefit from an accurate, non-invasive predictor of pCRthat facilitated more accurate and effective targeting of NAC.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate various example apparatus,methods, and other example embodiments of various aspects of theinvention. It will be appreciated that the illustrated elementboundaries (e.g., boxes, groups of boxes, or other shapes) in thefigures represent one example of the boundaries. One of ordinary skillin the art will appreciate that in some examples one element may bedesigned as multiple elements or that multiple elements may be designedas one element. In some examples, an element shown as an internalcomponent of another element may be implemented as an external componentand vice versa. Furthermore, elements may not be drawn to scale.

FIG. 1 illustrates textural features of a CT image of a granuloma and acarcinoma.

FIG. 2 illustrates an example method of characterizing a nodule in aregion of tissue.

FIG. 3 illustrates a perinodular zone associated with a nodule.

FIG. 4 illustrates an example method for segmenting a nodule.

FIG. 5 illustrates an example method of characterizing a nodule in aregion of lung tissue.

FIG. 6 illustrates an example method of characterizing a nodule in aregion of lung tissue.

FIG. 7 illustrates an example apparatus that classifies a region oftissue in an image.

FIG. 8 illustrates an example computer in which example methods andapparatus may operate.

DETAILED DESCRIPTION

Variations in tumor invasiveness or morphology may be related to patientprognosis and outcome. Conventional methods of diagnosing cancer includevisual histopathological examination of a biopsy to create a diagnosticprofile based on variations in tumor morphology or invasiveness.However, invasive biopsies and surgical procedures may not always be aconvenient or appropriate method for assessing nodules detected in aradiological image. Invasive biopsies and surgical resections costmoney, take time, and put a patient at additional risk. A non-invasiveapproach that provided improved accuracy compared to conventionalCT-based or MRI-based approaches would reduce the number of unnecessaryinterventions, reduce the dependency on repetitive or higher resolutionradiological exams, offer a non-invasive means of assessing response totargeted therapies, and improve patient outcomes. Thus, a timely,non-invasive procedure that results in more accurate discriminationbetween benign tissue and malignant tissue, or that more accuratelypredicts a response to treatment, would offer reduced risk to patientswhile providing economic benefits to the health care system.

CT imagery is conventionally used to differentiate malignant nodulesfrom other, non-cancerous nodules. A nodule may include a ground glassopacity (GGO) nodule or a solitary pulmonary nodule. However, it isdifficult to distinguish lung AC nodules from benign Gr nodules,including nodules secondary to histoplasmosis infection, since both ACnodules and Gr nodules can have similar appearances and both can showincreased activity on positron emission tomography (PET) or CTevaluation. For example, on chest a CT image, Gr nodules and AC nodulesmay both demonstrate a spiculated appearance. However, the vascularinvasion and lymphangiogenesis in the perinodular habitat of AC isdifferent from that of Gr. In particular, the perinodular zone orhabitat of a malignant nodule may exhibit different molecular,radiological, or cellular alterations than the perinodular zone of abenign nodule. Additionally, neoplastic infiltration of malignantnodules may distort neighboring tissue in the perinodular zone.Malignant AC may also demonstrate different histologic patterns thanbenign Gr, including different lepidic, acinar, papillary,micropapillary, or solid histologic patterns.

Conventional methods of visually assessing nodule invasiveness based onCT imagery are subjective and yield intra and inter-reviewervariability. In one study, for example, more than 30% of suspiciousnodules that underwent biopsy for histologic confirmation weredetermined to be benign Gr caused by prior histoplasmosis infection.Conventional CT approaches may focus exclusively on detection of lungnodules, or exclusively on diagnosing malignancy via CT scans. Exampleapparatus and methods discriminate benign Gr from malignant nodules byanalyzing features extracted from a perinodular region associated with anodule. The perinodular zone may be defined as the region surroundingthe nodule extending a threshold distance from the nodule boundary. Theperinodular zone may extend, in one embodiment, up to one centimeterfrom the nodule boundary. In other embodiments, the perinodular zone mayextend a different distance from the nodule boundary. The perinodularzone may also be referred to as a peri-tumoral zone, or a peri-diseasezone. For example, a region of tissue demonstrating a pathology may bebordered by a peri-disease zone extending a threshold distance from theregion of tissue demonstrating the pathology. Example methods andapparatus distinguish benign granulomas secondary to histoplasmosisfungal infection from malignant carcinomas, and provide decision supportin the diagnosis and treatment of patients exhibiting lung nodules inradiological imagery. Distinguishing benign fungal infection frommalignant carcinoma facilitates reducing the number of surgicalinterventions performed that ultimately result in a diagnosis ofhistoplasmosis or other non-cancerous pathology.

MR imagery is conventionally used to image breast cancer tissue. NAC isoften a first line of defense in the treatment of breast cancer. NAC isadministered prior to surgery, and may reduce the extent of tumor burdenand increase a patient's surgical options. The ideal outcome of NAC ispCR, which is strongly correlated with favorable prognosis and reducedrecurrence of breast cancer compared to patients who exhibit partial orno pCR. However, fewer than 25% of breast cancer patients who undergoNAC will achieve pCR. Thus, more accurate, non-invasive prediction ofpCR would offer reduced risk to patients while providing economicbenefits to the health care system. Example methods and apparatus thusfacilitate patients less likely to achieve pCR being spared costly,ineffective treatments, while patients more likely to achieve pCR may bemore likely to receive appropriate treatments.

The occurrence of pCR in NAC recipients is dependent on tumorcharacteristics. Furthermore, the tissue surrounding a breast tumor ornodule (e.g. the perinodular zone) includes useful markers of NACresponse. In particular, lymphocyte infiltration and immune responsewithin the stroma are predictive of pCR in all breast cancer subtypes.Example methods and apparatus employ dynamic contrast-enhanced (DCE) MRimaging to image breast tissue demonstrating cancerous pathology.Example methods and apparatus detect pathologic markers of pCR in DCE-MRimagery and predict patient response to NAC treatment based, at least inpart, on the detected pathologic markers. Example methods and apparatusdetect pathological markers of pCR in DCE-MR imagery of a patientdemonstrating breast cancer pathology by extracting and analyzingradiomic features from a perinodular zone associated with a tumorrepresented in the DCE MR imagery.

Example methods and apparatus more accurately distinguish malignant lungnodules from benign lung nodules by extracting and analyzing a set offeatures from a perinodular region associated with a lung nodulerepresented in a radiological image. Example methods and apparatus mayalso extract and analyze a set of features from the nodule to furtherdistinguish benign lung nodules from malignant lung nodules. Forexample, example methods and apparatus may compute a probability that anodule is a benign nodule based, at least in part, on the set offeatures extracted from the perinodular region, and the set of featuresextracted from the nodule. Since a more accurate distinction is made,example apparatus and methods thus predict patient outcomes in a moreconsistent and reproducible manner.

Example methods and apparatus predict patient outcomes more accuratelythan conventional methods by employing computerized textural andmorphologic analysis of lung CT imagery to distinguish benign Gr frommalignant tumors. Example methods and apparatus may segment a nodulefrom an image background. A spectral embedding gradient vector flowactive contour (SEGvAC) model may be employed to segment the nodule fromthe image background. A perinodular region may be defined with respectto the nodule segmented from the image background. The perinodularregion may extend a threshold distance from the nodule. Features may beextracted from the perinodular region. The features extracted from theperinodular region may include texture features. The texture featuresmay include gradient-based texture features. Malignant lung tumors mayinduce irregular changes to vessel shapes within the perinodular region.Example methods and apparatus also detect and quantify differences inlymphatic vessel density within the perinodular region. Example methodsand apparatus may also extract shape features or tortuosity featuresfrom the perinodular region, or from the nodule. Features extracted fromthe perinodular region or the nodule may facilitate improved detectionand analysis of histologic patterns demonstrated by AC or otherdiseases, including lepidic patterns, acinar patterns, papillarypatterns, micropapillary patterns, or solid patterns. Features extractedfrom the perinodular region or the nodule may facilitate capturinggrowth patterns of AC or other malignancies, including angiogenesis,tumor growth, invasion, or metastasis that constitute a neoplasticmicroenvironment around the nodule. A subset of extracted features maybe selected using principal component analysis (PCA)-variable importanceprojection (VIP) analysis. The subset of extracted features may includefeatures that are more discriminative than other, non-selected features.A classification of the nodule image may be generated using quadraticdiscriminant analysis (QDA) or linear discriminant analysis (LDA).

Carcinomas or other diseased tissue may have a more chaotic cellulararchitecture than Gr or other benign tissue. The chaotic cellulararchitecture may be correlated to an energy feature in an image. Theenergy feature may be represented as a texture feature. In someembodiments, the energy feature is more pronounced in a CT heatmap of acancerous nodule than in a CT heatmap of a Gr because of the morechaotic cellular architecture of the cancerous nodule. FIG. 1illustrates this property of cancerous nodules compared with Gr nodulesthat were caused by benign fungal infections. The chaotic cellulararchitecture may also be correlated to tortuosity features of vesselsassociated with a tumor or a nodule.

FIG. 1 illustrates example textural features that example methods andapparatus may use to distinguish benign nodules from malignant nodules.FIG. 1 illustrates a CT scan image 110 of a cancerous nodule identifiedas a carcinoma. FIG. 1 also illustrates a CT scan image 120 of a noduleidentified as a Gr. FIG. 1 also illustrates a close-up view 130 of thecancerous nodule, along with a first perinodular zone 135. FIG. 1 alsoillustrates a close up view 140 of the granuloma, along with a secondperinodular zone 145. FIG. 1 also illustrates a heatmap 150 of a Gaborfeature of the cancerous nodule. The Gabor feature represents textureusing a sinusoidal plane wave modulated Gaussian kernel function. FIG. 1further illustrates a heatmap 160 of a Gabor texture feature of thebenign granuloma. FIG. 1 also illustrates a scale 170 for reading theGabor texture features.

Example methods and apparatus may train and test a classifier. Forexample, one embodiment may employ 3-fold cross validation for traininga classifier and for testing the classifier. The classifier may be asupport vector machine (SVM) classifier. For example, a humanpathologist may manually delineate and classify one hundred nodules fora training set and thirty nodules for a testing set. Example methods andapparatus may classify the nodule image as a carcinoma, adenocarcinoma,or as a granuloma. Other classifications may be employed. Other sizes oftraining sets or sizes of testing sets may be employed. Example methodsand apparatus may classify the nodule as having a threshold probabilityof achieving pCR after NAC treatment.

Example methods and apparatus may employ an SVM classifier inconjunction with PCA-VIP determined features to discriminate pathologiesof interest (e.g. adenocarcinoma, granuloma, likely to achieve pCR). Theclassifier may be trained solely on the training set. A radial-basedkernel function (RBF) may be applied to the training set. Members of thetraining set are defined in instance-label form (x_(i),y_(i)) wherex_(i) 531 R^(n) and y_(i)∈{−1,1}. The RBF function is formally definedas:

K(x _(i) , x _(j))=exp(γ∥x _(i) −x _(j)∥²), γ>0.

Example methods and apparatus thus improve on conventional methods bymore accurately distinguishing between pathological and benign lungnodules. Example methods and apparatus distinguish granuloma fromcarcinoma with an accuracy of at least 0.77 area under the curve (AUC)when using texture features extracted from a smooth CT reconstructionkernel (rK) with a QDA SVM classifier. In contrast, conventionalapproaches using a sharp rK achieve only 0.72 AUC, conventionalapproaches using just Laws features achieve accuracies of approximately0.61 AUC, while conventional approaches using just Gabor featuresachieve accuracies of approximately 0.68 AUC. Example methods andapparatus thus facilitate a significant, measurable increase in accuracycompared to conventional approaches. Example methods and apparatus mayalso predict response to NAC with an accuracy of at least 0.87 AUC.

By increasing the accuracy with which malignant nodules aredistinguished from benign lung nodules, or by which response totreatment is predicted, example methods and apparatus produce theconcrete, real-world technical effect of reducing the time required toevaluate medical imagery while increasing the accuracy of theevaluation. Additionally, example apparatus and methods increase theprobability that at-risk patients receive timely treatment tailored tothe particular pathology they exhibit. Example methods and apparatus mayalso reduce the number of invasive procedures needed to accuratelycharacterize nodules. The additional technical effect of reducing theexpenditure of resources and time on patients who are less likely tosuffer recurrence or disease progression is also achieved. Examplemethods and apparatus thus improve on conventional methods in ameasurable, clinically significant way.

Some portions of the detailed descriptions that follow are presented interms of algorithms and symbolic representations of operations on databits within a memory. These algorithmic descriptions and representationsare used by those skilled in the art to convey the substance of theirwork to others. An algorithm, here and generally, is conceived to be asequence of operations that produce a result. The operations may includephysical manipulations of physical quantities. Usually, though notnecessarily, the physical quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated in a logic, and so on. The physicalmanipulations create a concrete, tangible, useful, real-world result.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, and so on. It should be borne in mind,however, that these and similar terms are to be associated with theappropriate physical quantities and are merely convenient labels appliedto these quantities. Unless specifically stated otherwise, it isappreciated that throughout the description, terms including processing,computing, calculating, determining, and so on, refer to actions andprocesses of a computer system, logic, processor, or similar electronicdevice that manipulates and transforms data represented as physical(electronic) quantities.

Example methods may be better appreciated with reference to flowdiagrams. While for purposes of simplicity of explanation, theillustrated methodologies are shown and described as a series of blocks,it is to be appreciated that the methodologies are not limited by theorder of the blocks, as some blocks can occur in different orders and/orconcurrently with other blocks from that shown and described. Moreover,less than all the illustrated blocks may be required to implement anexample methodology. Blocks may be combined or separated into multiplecomponents. Furthermore, additional and/or alternative methodologies canemploy additional, not illustrated blocks.

FIG. 2 illustrates an example computerized method 200 for characterizinga nodule in a region of tissue. Method 200 includes, at 210, accessingan image of a region of tissue. Accessing the image may includeaccessing a no-contrast CT image of a region of lung tissuedemonstrating cancerous pathology. Accessing the image may also includeaccessing another type of medical image of a region of tissuedemonstrating a different, non-cancerous pathology. Accessing the imagemay include retrieving electronic data from a computer memory, receivinga computer file over a computer network, or other computer or electronicbased action. In one embodiment, the image is a 1 mm to 5 mm thick,no-contrast chest CT image. In one embodiment, the number of slices perscan may range from 126 to 385, and a slice may have an XY planarresolution of 512 pixels by 512 pixels, with a 16 bit gray scaleresolution indicated in Hounsfield Units (HU). In another embodiment,other image types, resolutions, scales, slices per scan, or imagedimensions may be used.

Method 200 also includes, at 220, segmenting a nodule in the image.Segmenting the nodule includes defining a nodule boundary. The noduleboundary may be extracted from the image. The nodule may beautomatically segmented by distinguishing nodule tissue within the imagefrom the background of the image. In one embodiment, the nodule tissuemay be automatically distinguished using SEGvAC segmentation. In anotherembodiment, other segmentation approaches may be employed, includingthreshold based segmentation, deformable boundary models,active-appearance models, active shape models, graph based modelsincluding Markov random fields (MRF), min-max cut approaches, or otherimage segmentation approaches. In another embodiment, the nodule may bemanually segmented.

SEGvAC segmentation includes separating the lung region from thesurrounding anatomy in the image. A non-linear embedding representationof the lung may be employed to separate the image of the lung from thesurrounding thoracic anatomy. The SEGvAC approach also includes removingnon-nodule structures from the image using a rule-based classifier. TheSEGvAC approach further includes extracting the nodule surface from theimage using active contour based segmentation. Example methods andapparatus employing a SEGvAC approach improve on conventional approachesby eliminating segmentation errors caused by both pleura and vesselattached nodules by separating lung tissues and removing non-nodulestructures.

SEGvAC segmentation employs a spectral embedding based active contour.Spectral embedding (SE) is a non-linear dimensional reduction methodthat forms an affinity matrix via a pre-specified kernel function. Thekernel function facilitates a mapping of an original set of imagefeatures or intensities to a new kernel space where spectraldecomposition may be applied to the corresponding graph Laplacian. Anindividual pixel from the original lung CT image is then represented bythe corresponding value of the eigenvectors obtained by spectraldecomposition. SE representation of the lung provides strong gradientsat the margin of the nodules which facilitate an active contour model tostop evolving at the nodule boundary. The SEGvAC approach employed byexample methods and apparatus further includes a gradient vector flowfield (GVF) active contour. The GVF forces are calculated for the imagedomain. The GVF forces drive the active contour.

In one embodiment, the SEGvAC segmentation approach includes isolatinglung regions from surrounding anatomy illustrated in the CT image togenerate an initial lung mask. Example methods and apparatus identify anoptimal threshold to separate body voxels from non-body voxels. Anon-body voxel is a low density voxel representing lung and surroundingair. The initial lung mask is further refined by applying morphologicalhole filling to the logical complement of the initial lung mask.

Upon extraction of the initial region of interest (e.g. lung region)from the CT image, example methods and apparatus may perform anautomatic segmentation of the nodule. Example methods and apparatusemploy an active contour scheme to segment the nodule. In oneembodiment, the image plane Ω=R² is partitioned into two regions by acurve γ. The foreground region of the image plane is defined as Ω₁ andthe background region of the image plane is defined as Ω₂. Thus, theimage plane is comprised of the union of regions of interest,background, and evolving contour (Ω=Ω₁∪Ω₂∪γ).

In simplified form, the energy functional of an edge-based activecontour is defined as

E=αE ₁ +βE ₂   (eq. 1)

where E₂ refers to internal forces used to keep the integrity andelasticity of the contour and where E₁ is the image force.

The image force E₁ is defined as

E ₁=∫_(γ) g(ν(c))dc   (eq. 2)

where c=(x,y) corresponds to a voxel in the two dimensional (2D) imageplane, ν(c) is the intensity value of the voxel c, and g(ν(c)) isdefined as

$\begin{matrix}{{g\left( {v(c)} \right)} = {\frac{1}{1 + {\psi \left( {v(c)} \right)}}.}} & \left( {{eq}.\mspace{14mu} 3} \right)\end{matrix}$

The gradient function ψ(ν(c)) is conventionally calculated by a graylevel gradient. Example methods and apparatus employ a tensor gradientfunction derived from the spectral embedding representation. By usingthe tensor gradient function, example methods and apparatus facilitatethe generation of improved region and boundary-based statistics, andstronger gradients, compared to conventional approaches.

Example methods and apparatus employ a GVF active contour. The GVFforces calculated for the image domain are used to drive the activecontour. Active contours driven by GVF forces do not need to beinitialized very closely to the boundary. The GVF forces are calculatedby applying generalized diffusion equations to both components of thegradient of an image edge map, where the image edge map is of theoriginal CT image. In one embodiment, the SEGvAC approach is initializedusing a single point and click on a region of interest (e.g. nodule). Inanother embodiment, the SEGvAC approach may be initializedautomatically.

In one embodiment, before employing the SEGvAC approach, example methodsand apparatus may employ a rule-based classifier to remove unwantedstructures from the image based on geometric properties of the unwantedstructures. The geometric properties of the unwanted structures may be3D geometric properties. The 3D geometric properties may includebounding box measures and elongation of 3D structures defined as thelength of the major axis of the nodule divided by the length of theminor axis of the nodule. Lung nodules are frequently 5 mm to 30 mmlong. Thus, 3D structures that do not fit this size may be eliminatedusing the rule-based classifier. Candidate objects for inclusion orexclusion may be examined in terms of convexity or elongation measuresfor distinguishing vessel-like structures from more convex orsphere-like objects. In one embodiment, a set of morphologicaloperations, including erosion and closing operations, may be employed tofilter objects associated with vessel-connected nodules. By removingunwanted structures, example methods and apparatus facilitate improvingthe performance of a computer aided diagnosis (CADx) system by reducingthe computational resources required to analyze the perinodular zone.

Method 200 also includes, at 230, defining a perinodular regionassociated with the nodule. In one embodiment, defining the perinodularregion includes generating an outer perinodular boundary by dilating thenodule boundary a threshold amount. In one embodiment, the thresholdamount is from 5 mm to 7 mm. In another embodiment, another, differentthreshold amount may be used. For example, the threshold amount may befrom 3.5 mm to 5 mm. In one embodiment, the threshold amount is useradjustable. The threshold amount may be based on a unit of distance(e.g. mm) or may be based on a pixel size, an image resolution, a numberof pixels, or other unit of measurement. For example, in one embodimentin which the CT image has a pixel size of 0.7 mm center to center, thethreshold amount may be defined as 7 pixels. Thus, in this example, amask of the nodule defined by the nodule boundary may be dilated byseven pixels. Defining the perinodular region further includessubtracting the nodule from the region defined by the outer perinodularboundary. Thus, for example, in one embodiment, the perinodular regionmay be bounded by the outer perinodular boundary and the noduleboundary.

FIG. 3 illustrates an example perinodular region 340 associated with anodule 310. Perinodular region 340 is bounded by outer perinodularboundary 330 and nodular boundary 320. In one embodiment, examplemethods and apparatus dilate nodule boundary 310 by an amount 350,resulting in the outer perinodular boundary 330.

In another embodiment, the perinodular boundary may be generating usingother techniques. For example, the perinodular boundary may be definedas a function of a property of the nodule. The property of the nodulemay include, for example, a diameter, a radius, a perimeter, an area, avolume, or other property of the nodule. The function may define theperinodular region as, for example, a dilation of the nodule boundary,where the dilation ratio is defined by a magnitude of an axis of thenodule. In another embodiment, the perinodular boundary may be definedas a disc of a threshold radius defined about the centroid of thenodule, or defined on the focal points of an elliptical representationof the nodule. In one embodiment, the perinodular boundary may bemanually defined. Other approaches or combinations of approaches may beused to define the perinodular boundary.

In one embodiment, method 200, at 230, includes removing pixels havingless than a threshold level of HU from the perinodular zone. Lungparenchyma have HU values of approximately −500. In one embodiment, thethreshold level is −900 HU. Removing pixels having less than a thresholdlevel of HU from the perinodular zone facilitates radiomic analysis ofthe perinodular zone by removing confounding information from the imagebeing analyzed, or by reducing the amount of computational resourcesrequired to extract features from the perinodular zone compared toconventional approaches. For example, pixels representing air, which hasan HU value of approximately −1000, may be removed from the image. Othertissue, including bone, may also be removed. For example, pixelsrepresenting cancellous bone (+700 HU) or cortical bone (+3000 HU) maybe removed.

Method 200 also includes, at 240, generating a set of perinodulartexture features from the perinodular region associated with the nodule.Generating the set of perinodular texture features includes extracting aset of texture features from the perinodular region. The set of texturefeatures includes a gray-level statistical feature, a steerable Gaborfeature, a histogram of oriented gradient (HoG) feature, a Haralickfeature, a Law feature, a Law-Laplacian feature, a local binary pattern(LBP) feature, an inertia feature, a correlation feature, a differenceentropy feature, a contrast inverse moment feature, or a contrastvariance feature. In one embodiment, the set of texture featuresincludes at least twenty four texture features. In other embodiments,other numbers or types of texture features may be extracted. The set oftexture features may also include a co-occurrence of local anisotropicgradient orientations (CoLIAGe) features.

In one embodiment, generating the set of perinodular texture featuresincludes selecting a subset of texture features from the set of texturefeatures. In one embodiment, the subset of texture features is selectedby reducing the set of texture features using a PCA-VIP approach. Inanother embodiment, first order statistics may be derived from differentradiomic descriptor families (e.g. Haralick, Laws Energy, HoG, orGabor). The subset of texture features may be selected after runningone-hundred iterations of three-fold cross validation using an AreaUnder the receiver-operating characteristic Curve (AUC) using a QDAclassifier. The most discriminative features may then be identifiedusing a Feed Forward Feature Selection (FFFS) approach. In oneembodiment, the subset of texture features includes a kurtosis of aHaralick feature, a mean of the Haralick feature, a kurtosis of aLaplacian, and a mean of a Law feature. In another embodiment, thesubset of texture features includes a mean of a Gabor feature, astandard deviation of the Gabor feature, a mean of the Gabor feature,and a median of an HoG.

In another embodiment, example methods and apparatus employ a PCA of theset of texture features to select the subset of texture features fromthe set of texture features. The subset of texture features may achievea threshold level of discriminability. For example, the PCA may selectone energy feature and one Gabor feature that are the mostdiscriminative, based on a particular set of CT images, fordistinguishing Gr from AC. The subset of texture features may include asfew as two texture features. The level of discriminability may be useradjustable. For example, in a first clinical situation, a subset oftexture features that achieves 0.84 AUC accuracy in distinguishingbenign Gr from AC may be acceptable. In another embodiment, a 0.77 AUCmay be acceptable. A feature may be considered to have a desirable levelof discriminability when the means of two separate classes are more thana threshold distance from each other, and where the variance of a classis less than a threshold distance, in comparison to the distance betweenthe means. In one embodiment, the Fisher criterion, which is the squareddifference of the means divided by the sum of the variances, may be usedto quantitatively establish a desirable level of discriminability.

Method 200 also includes, at 250, computing a probability that thenodule is benign. Method 200 computes the probability based, at least inpart, on the set of perinodular texture features. In one embodiment,computing the probability that the nodule is benign includes computingthe probability that the nodule is a benign Gr secondary tohistoplasmosis infection. In another embodiment, computing theprobability that the nodule is benign includes computing the probabilitythat the nodule is another type of benign nodule. Example methods andapparatus may also compute a probability that the nodule is malignant.Example methods and apparatus may also compute a probability that thenodule will achieve pCR in response to NAC. Example methods andapparatus may also compute a probability that a different type ofcancerous pathology identified in the image will respond to a differenttreatment.

Method 200 also includes, at 260, classifying the nodule. Classifyingthe nodule may include controlling a CADx system to generate aclassification of the nodule represented in the image. Theclassification may be based, at least in part, on the set of perinodulartexture features or the probability. In one embodiment, the CADx systemgenerates the classification of the image of the nodule using a QDAclassifier. In another embodiment, the CADx system may generate theclassification using other, different types of classifier. Theclassifier may be an SVM classifier trained and tested on a set ofimages of pre-classified nodules. The set of images of pre-classifiednodules may include an image of a region of tissue demonstratingadenocarcinoma pathology annotated by an expert pathologist. In oneembodiment, controlling the CADx system to generate the classificationof the nodule based, at least in part, on the set or perinodular texturefeatures or the probability, includes classifying the image of thenodule as malignant adenocarcinoma or benign Gr secondary tohistoplasmosis infection. In another embodiment, example methods andapparatus control the CADx system to generate a classification of thenodule based, at least in part, on the set of perinodular texturefeatures, or on the probability that the nodule will achieve pCR.

Example methods and apparatus facilitate more accurate characterizationof nodules found in CT images than conventional approaches. Examplemethods and apparatus thus improve on conventional methods bycharacterizing nodules as benign Gr secondary to histoplasmosisinfection, carcinomas, or adenocarcinomas, with greater accuracy andwith less subjective variability than conventional methods. Examplemethods and apparatus therefore facilitate more judicious application ofbiopsies and surgical resection in a population undergoing CT screeningfor lung cancer. Example methods and apparatus also facilitate moreaccurate prediction of achieving pCR from DCE-MR imagery of tissuedemonstrating breast cancer pathology. Example methods and apparatustherefore facilitate more efficient and accurate targeting andapplication of NAC treatment.

Using a more appropriately determined and applied treatment may lead toless therapeutics being required for a patient or may lead to avoidingor delaying a biopsy, a resection, or other invasive procedure. Whenregions of cancerous tissue, including nodules detected in CT scans, ornodules detected in DCE-MRI are more quickly and more accuratelyclassified, patients with poorer prognoses may receive a higherproportion of scarce resources (e.g., therapeutics, physician time andattention, hospital beds) while those with better prognoses may bespared unnecessary treatment, which in turn spares unnecessaryexpenditures and resource consumption. Example methods and apparatus maythus have the real-world, quantifiable effect of improving patientoutcomes.

While FIG. 2 illustrates various actions occurring in serial, it is tobe appreciated that various actions illustrated in FIG. 2 could occursubstantially in parallel. By way of illustration, a first process coulddelineate a nodule in a CT image, a second process could define aperinodular zone in the CT image, and a third process could extractperinodular texture features from the CT image. While three processesare described, it is to be appreciated that a greater or lesser numberof processes could be employed and that lightweight processes, regularprocesses, threads, and other approaches could be employed.

In one embodiment, method 200 may also include other steps. Method 200may include accessing a DCE-MR image of a region of breast tissue.Method 200 may also include other steps when defining the perinodularregion. In one embodiment, defining the perinodular region includessegmenting a breast wall from the image, and removing or subtracting thesegmented breast wall from the image. Segmenting the breast wall may beperformed manually, or may be performed automatically. Breast cancernodules may occur within a threshold distance to the breast wall.Generating the perinodular zone may thus include surrounding empty spaceor the hyper intense interface between breast and air. Example methodsand apparatus segment the breast wall to exclude areas outside thebreast from the perinodular zone, thus increasing the accuracy withwhich nodules may be characterized, and reducing the computationalresources required to extract and analyze features from the perinodularzone.

In one embodiment, method 200 generates the outer perinodular boundaryby dilating the nodule boundary a threshold amount. For example, whenanalyzing breast cancer tissue, the perinodular boundary may be dilateda threshold amount of 3.5 mm to 5 mm, or may be dilated from 7 mm to 10mm. The threshold amount may be determined as a function of tumor size,invasiveness, or other factors. Method 200 may also generate aperinodular region by subtracting the nodule from the region defined bythe outer perinodular boundary.

In one embodiment, method 200 computes a probability that the nodulewill achieve pCR based, at least in part, on the set of perinodulartexture features. In one embodiment the set of perinodular texturefeatures may be extracted from the DCE-MR image at two post-contrastphases. The set of perinodular texture features may be extracted at afirst, initial post-contrast phase, including a first scan collectedfollowing intravenous contrast agent injection. The set of perinodulartexture features may also be extracted during a second, later phase inwhich the contrast enhancement intensity is at a threshold peak contrastlevel. In one embodiment, the set of perinodular texture featuresextracted during the initial post-contrast phase includes a Lawsspot-wave standard deviation feature, a CoLIAGe sum entropy-kurtosisfeature, a Laws wave-wave skewness feature, a Laws wave-wave standarddeviation feature, and a Gabor standard deviation feature. In oneembodiment, the set of perinodular features extracted during the peakcontrast phase includes, a CoLIAGe difference-variance skewness feature,a Laws edge-ripple standard deviation feature, a Haralick inversedifference moment skewness feature, a Laws level-ripple mean feature,and a CoLIAGe difference entropy kurtosis feature. CoLIAGe featuresinclude statistics of dominant gradient orientation co-occurrencematrices. Haralick calculations computed on first derivative gradientorientations may be extracted from the CoLIAGe features. Other featuresmay also be extracted, and other statistics may be calculated.

In one embodiment, method 200 also controls the CADx system to generatea classification of the nodule. The classification of the nodule may bebased, at least in part, on the set of perinodular texture features, orthe probability that the nodule will achieve pCR. The classification ofthe nodule facilitates the timely, efficient, and accurate applicationof NAC.

FIG. 4 illustrates an example method 400 for distinguishing noduletissue in a CT image from a background of the image using a SEGvACapproach. Method 400 is suitable for use by example methods andapparatus described herein, including method 200, method 500, method600, or apparatus 700. Method 400 includes, at 410 generating an initiallung mask. In one embodiment, generating the initial lung mask includesseparating a lung region represented in the image from surrounding lunganatomy. In one embodiment, generating the initial lung mask includesrefining the initial lung mask by applying morphological hole-filling toa logical complement of the initial lung mask.

Method 400 also includes, at 420, generating a refined image. Generatingthe refined image includes removing a non-granuloma structure from theinitial lung mask using a rule-based classifier. In one embodiment, therule-based classifier selects a non-granuloma structure to remove fromthe initial lung mask based on a convexity measure of the non-granulomastructure, or on an elongation measure of the non-granuloma structure.The rule-based classifier may select a non-granuloma structure to removebased on 3D geometric properties of structures in the perinodular zone.The 3D properties may include bounding box measures and elongation ofthe 3D structure defined as the length of the major axis divided by thelength of the minor axis. In one embodiment, 3D structures that do notfit within a size criteria range of 5 mm to 30 mm are removed by therule-based classifier. In another embodiment, morphological operations,including erosion operations or closing operations, are used to isolatevessel-connected nodules.

Method 400 also includes, at 430, generating a spectral embedding (SE)representation by projecting at least one refined image into a 3D SEspace. In one embodiment, generating an SE representation includesforming an affinity matrix via a pre-specified kernel function. Thekernel function facilitates mapping a set of image features to a newkernel space, where spectral decomposition is applied to a correspondinggraph Laplacian. A pixel in the CT image is then represented by acorresponding value of an eigenvector obtained via the spectraldecomposition step.

Method 400 also includes, at 440, extracting a nodule boundary from theSE representation. Extracting the nodule boundary may includecalculating a tensor gradient function derived from the SErepresentation. In one embodiment, extracting the nodule boundary fromthe SE representation includes extracting the nodule boundary using agradient vector flow field (GVF) active contour model. A GVF forcedrives the active contour. In one embodiment, the GVF force iscalculated based on a generalized diffusion equation applied to acomponent of an image edge map of the CT image of the region of lungtissue.

FIG. 5 illustrates an example method 500 for characterizing a nodule ina region of lung tissue. Method 500 is similar to method 200 butincludes additional actions. Method 500 includes actions 510, 520, 530,and 540 which are similar to actions 210, 220, 230, and 240 describedabove with respect to method 200.

Method 500 also includes, at 542, extracting a set of nodule featuresfrom the nodule. In one embodiment, extracting the set of nodulefeatures includes extracting a set of shape features from the image ofthe nodule. The set of shape features includes a location feature, asize feature, a width feature, a height feature, a depth feature, aperimeter feature, an eccentricity feature, an eccentricity standarddeviation, a compactness feature, a roughness feature, an elongationfeature, a convexity feature, an extend feature, an equivalent diameterfeature, or a sphericity feature. The location feature describes thespatial information of a pixel in the image of the nodule, the sizefeature describes the number of pixels within the segmented image of thenodule, and the perimeter feature describes the distance around theboundary of the segmented nodule. The eccentricity feature describes theeccentricity of an ellipse that has the same second moments as thenodule. The compactness feature describes the isoperimetric quotient ofthe nodule. The roughness feature describes the perimeter of a lesion ina slice of the image of the nodule divided by the convex perimeter ofthe lesion. The elongation feature describes the ratio of minor axis tothe major axis of the image of the nodule, and the convexity featuredescribes the ratio of a tumor image slice to the convex hull of thetumor. The extend feature describes the ratio of pixels in the tumorregion to pixels in the total bounding box. The equivalent diameterfeature describes the diameter of a circle having the same area as atumor image slice, and the sphericity feature describes thethree-dimensional compactness of the nodule. In one embodiment the setof shape features includes at least twenty-four shape features. Inanother embodiment, the set of shape features may include other numbersof shape features, or other, different shape features. A feature may becalculated in 3D space, or in two dimensional (2D) space. For example,width, height, depth, or sphericity features may be calculated in 3Dspace.

In one embodiment, extracting the set of nodule features from the noduleincludes extracting a second set of texture features. The second set oftexture features includes a gray-level statistical feature, a steerableGabor feature, a Haralick feature, a Law feature, a Law-Laplacianfeature, a local binary pattern (LBP) feature, inertia, a correlationfeature, a difference entropy feature, a contrast inverse momentfeature, or a contrast variance feature. In one embodiment, the secondset of texture features includes at least twenty four texture features.In other embodiments, other numbers or types of texture features may beextracted.

Method 500 also includes, at 544, generating a reduced set of nodulefeatures. In one embodiment, generating a reduced set of nodule featuresincludes selecting a subset of shape features from the set of shapefeatures. In one embodiment, the subset of shape features includeseccentricity, eccentricity standard deviation, or elongation features.In another embodiment, the subset of shape features may include other,different shape features. The subset of shape features may be selectedfrom the set of shape features using PCA feature ranking or PCA-VIPfeature ranking.

Method 500 also includes, at 550, computing a probability that thenodule is benign. In one embodiment, computing the probability that thenodule is benign includes computing the probability that the nodule is abenign Gr secondary to histoplasmosis infection. In another embodiment,computing the probability that the nodule is benign includes computingthe probability that the nodule is another type of benign nodule.Example methods and apparatus may also compute a probability that thenodule is malignant. Example methods and apparatus may also compute aprobability that the nodule will achieve pCR in response to NAC. Examplemethods and apparatus may also compute a probability that a differenttype of cancerous pathology identified in the image will respond to adifferent treatment. Method 500 computes the probability based, at leastin part, on the reduced set of nodule features and the set ofperinodular texture features.

Method 500 also includes, at 560, controlling the CADx system togenerate a classification of the image of the nodule. In one embodiment,the CADx system classifies the nodule as a benign Gr secondary tohistoplasmosis infection, or as a malignant adenocarcinoma. The CADxsystem may employ an SVM to generate the classification. Theclassification may be based, at least in part, on the subset of texturefeatures and the subset of shape features. Basing the classification onboth the subset of texture features and the subset of shape featuresimproves on conventional approaches by increasing the accuracy withwhich the image of the may be classified. In one embodiment, the CADxsystem generates the classification of the image of the nodule using anLDA classifier or a QDA classifier. The LDA classifier or the QDAclassifier may be trained or tested on a set of images pre-classified asGr or adenocarcinoma.

In one embodiment, example methods and apparatus may also automaticallysegment vessels associated with the nodule. For example, method 500 mayidentify a centerline of a vessel and branching points associated withthe vessel. Method 500 may identify the centerline or branching pointsusing a fast marching approach. Method 500 calculates the torsion for avessel segment using a distance metric. The torsion of a vessel segmentis defined as 1-(Distance/Length) where distance is the Euclideandistance of the start and end point of the segment, and where length isthe number of voxels along the vessel segment. Method 500 also extractsthe curvature of a vessel segment. Curvature at a voxel of a vesselsegment is proportional to the inverse of an osculating circle's radius.The osculating circle is fitted to a collection of three neighboringpoints along the centerline of a vessel. For a plurality of points alongthe center line of a vessel, method 500 fits a circle to compute thecurvature of a specific point. Method 500 then computes mean andstandard deviation of the curvature for points along the vessel. Method500 may also capture branching statistics associated with the vessel.

Method 500 may then extract a set of tortuosity features from the imageof the nodule. The tortuosity features describe vessels associated withthe nodule. The set of tortuosity features includes the mean of torsionof a vessel segment, or the standard deviation of torsion of a vesselsegment. The set of tortuosity features also includes the mean andstandard deviation of the mean curvature of a group of vessel segments.The set of tortuosity features also includes the mean and standarddeviation of the standard deviation of a vessel segment curvature and atotal vessel segment length. The set of tortuosity features may alsoinclude branching statistics associated with the vessel. In oneembodiment, the set of tortuosity features includes at least seventortuosity features. In another embodiment, the set of tortuosityfeatures may include other numbers of tortuosity features, or other,different tortuosity features. Method 500 may also select of subset oftortuosity features from the set of tortuosity features. Method 500 mayalso include controlling the CADx system to generate the classificationof the image of the nodule based, at least in part, on the subset oftortuosity features, the subset of texture features and the subset ofshape features.

FIG. 6 illustrates an example method 600 for distinguishing benigntumors from malignant tumors in chest CT images. Method 600 includes, at610 accessing an image of a region of tissue demonstrating cancerouspathology. In one embodiment, the image is a 1 mm to 5 mm thick,no-contrast chest CT image. In another embodiment, other image types orimage dimensions may be used. Accessing the image may include retrievingelectronic data from a computer memory, receiving a computer file over acomputer network, or other computer or electronic based action.

Method 600 also includes, at 620, segmenting a nodule in the image fromthe background of the image. Segmenting the nodule in the image from thebackground of the image involves identifying the portion of the imagethat represents the nodule to distinguish that portion from thebackground. In one embodiment, the nodule is automatically segmentedfrom the background of the image. In another embodiment, a humanpathologist manually delineates the nodule from the background of theimage. In another embodiment, vessels associated with the nodule arealso segmented. The nodule may be segmented using SEGvAC segmentation.

Method 600 also includes, at 630, defining a perinodular region. Theperinodular region may be based, at least in part, on the nodule. Theperinodular region may be defined by dilating a boundary of the nodule,and subtracting the nodule from the region defined by the dilatedboundary of the nodule.

Method 600 also includes, at 640, selecting a first set of texturefeatures from the perinodular region. In one embodiment, the first setof texture features may include a gray-level statistical feature, asteerable Gabor feature, a Haralick feature, a Law feature, aLaw-Laplacian feature, an LBP feature, an inertia feature, a correlationfeature, a difference entropy feature, a contrast inverse momentfeature, or a contrast variance feature. In another embodiment, other,different texture features may be selected.

Method 600 also includes, at 650, selecting a second set of texturefeatures or a set of shape features from the nodule. The set of shapefeatures may include a location feature, a size feature, a perimeterfeature, an eccentricity feature, an eccentricity standard deviation, acompactness feature, a roughness feature, an elongation feature, aconvexity feature, an equivalent diameter feature, a radial distancefeature, an area feature, or a sphericity feature. The second set oftexture features may include a gray-level statistical feature, asteerable Gabor feature, a Haralick feature, a Law feature, aLaw-Laplacian feature, an LBP feature, an inertia feature, a correlationfeature, a difference entropy feature, a contrast inverse momentfeature, or a contrast variance feature. In another embodiment, other,different texture features may be selected.

In one embodiment, method 600 may also include selecting a set oftortuosity features from the perinodular region. The set of tortuosityfeatures may include the mean of torsion of a vessel segment, or thestandard deviation of torsion of a vessel segment. The set of tortuosityfeatures may also include the mean and standard deviation of the meancurvature of a group of vessel segments. The set of tortuosity featuresmay also include the mean and standard deviation of the standarddeviation of a vessel segment curvature and a total vessel segmentlength. In one embodiment, the set of tortuosity features includes atleast seven tortuosity features. In another embodiment, the set oftortuosity features may include other numbers of tortuosity features, orother, different tortuosity features.

Method 600 also includes, at 660, generating a probability that thenodule is a benign Gr. Generating the probability may include generatinga classification for the nodule based, at least in part, on the firstset of texture features and the second set of texture features, the setof shape features, or the set of tortuosity features. In one embodiment,the classification is made based on the first set of texture features.In another embodiment, the classification is based on the set of shapefeatures. In still another embodiment, the classification is based on asubset of the first set of texture features, a subset of the set ofshape features, a subset of the second set of texture features, and asubset of the set of tortuosity features. The subset of the first set oftexture features may be selected from the first set of texture featuresusing PCA-VIP or PCA. The subset of the second set of texture featuresmay be selected from the second set of texture features using PCA-VIP orPCA. The subset of the set of shape features may be selected from theset of shape features using PCA-VIP or PCA. The subset of the set oftortuosity features may be selected from the set of tortuosity featuresusing PCA or PCA-VIP. The subset of shape features, the subset of thefirst set texture features, the subset of the second set of texturefeatures, or the subset of tortuosity features may be selected toachieve a threshold level of accuracy when classifying tumors. In oneembodiment, method 600 classifies the tumor as a carcinoma or a Gr. Inanother embodiment, the tumor is classified as frank invasive, minimallyinvasive, or non-invasive. The classification may be made by a CADxsystem using an SVM, a QDA classifier, or an LDA classifier.

Method 600 also includes, at 670, providing a prognosis prediction basedon the probability. For example, method 600, at 660, may provide aprobability that the nodule is benign, and method 600 at 670, mayprovide a prognosis prediction based on the probability. Method 600 may,alternately, provide a probability that the nodule is malignant.

In one example, a method may be implemented as computer executableinstructions. Thus, in one example, a computer-readable storage mediummay store computer executable instructions that if executed by a machine(e.g., computer) cause the machine to perform methods described orclaimed herein including method 200, method 400, method 500, and method600. While executable instructions associated with the listed methodsare described as being stored on a computer-readable storage medium, itis to be appreciated that executable instructions associated with otherexample methods described or claimed herein may also be stored on acomputer-readable storage medium. In different embodiments the examplemethods described herein may be triggered in different ways. In oneembodiment, a method may be triggered manually by a user. In anotherexample, a method may be triggered automatically.

FIG. 7 illustrates an example apparatus 700 for classifying a region oftissue in an image. Apparatus 700 includes a processor 710, a memory720, an input/output (I/O) interface 730, a set of logics 750, and aninterface 740 that connects the processor 710, the memory 720, the I/Ointerface 730, and the set of logics 750. The set of logics 750 includesan image acquisition logic 751, a delineation logic 753, a perinodularzone logic 755, a feature extraction logic 757, a probability logic 758,and a classification logic 759. In one embodiment, the functionalityassociated with the set of logics 750 may be performed, at least inpart, by hardware logic components including, but not limited to,field-programmable gate arrays (FPGAs), application specific integratedcircuits (ASICs), application specific standard products (ASSPs), systemon a chip systems (SOCs), or complex programmable logic devices (CPLDs).In one embodiment, individual members of the set of logics 750 areimplemented as ASICs or SOCs.

Image acquisition logic 751 acquires an image of a region of tissue. Theimage may be acquired from, for example, a CT apparatus. The region oftissue may be a section of tissue demonstrating cancerous pathology in apatient. The image of the region of tissue may include an image of anodule. In one embodiment, the image is a 1 mm to 5 mm thick,no-contrast chest CT image with a pixel size of 0.7 mm center to center.Other imaging approaches may be used to generate and access the imageaccessed by image acquisition logic 751. Other image dimensions, pixelsizes, or resolutions may also be used. In one embodiment, the image isacquired from a DCE-MR imaging apparatus, and the region of tissue is aregion of breast tissue demonstrating breast cancer pathology.

Delineation logic 753 automatically delineates the nodule bydistinguishing nodule tissue within the image from the background of theimage. In one embodiment, delineation logic 753 automatically delineatesthe nodule using SEGvAC segmentation. In another embodiment, delineationlogic 753 automatically delineates the nodule using threshold basedsegmentation, deformable boundary models, active-appearance models,active shape models, graph based models including Markov random fields(MRF), min-max cut approaches, or other image delineation approaches. Inone embodiment, delineation logic 753 is configured to facilitate ahuman radiologist delineating the nodule. In one embodiment, delineationlogic 753 segments tumor tissue from other, non-tumor tissue in an imageof a breast demonstrating cancerous pathology. Delineation logic 753 mayalso segment breast wall tissue represented in the image. Delineationlogic 753 may define a nodule boundary.

Perinodular zone logic 755 defines a perinodular zone based, at least inpart, on the nodule tissue. In one embodiment, perinodular zone logic755 defines the perinodular zone by defining a perinodularzone-plus-nodule region by dilating the nodule boundary a thresholdamount, and subtracting the nodule region from the perinodularzone-plus-nodule region. In one embodiment, the threshold amount iswithin the range (4.9 mm, 7.0 mm). In another embodiment, perinodularzone logic 755 defines the perinodular zone using other ranges ortechniques. For example, perinodular zone logic 755 may define theperinodular zone using a threshold amount within the range (3.5 mm, 5mm) or the range (7 mm, 10 mm). In one embodiment, perinodular zonelogic 755 may define the perinodular zone by subtracting the breast walltissue from the image.

Feature extraction logic 757 extracts a set of features from the image.The set of features includes a first set of texture features, a set ofshape features, or a second set of texture features. The first set oftexture features or the set of shape features may be extracted from thenodule tissue in the image of the delineated nodule. The second set oftexture features is extracted from the perinodular region. In oneembodiment, the first set of texture features or the second set oftexture features include a gray-level statistical feature, a steerableGabor feature, a Haralick feature, a Law feature, a Law-Laplacianfeature, an LBP feature, an inertia feature, a correlation feature, adifference entropy feature, a contrast inverse moment feature, a CoLIAGefeature, or a contrast variance feature. The set of shape features mayinclude a location feature, a size feature, a perimeter feature, aneccentricity feature, an eccentricity standard deviation, a compactnessfeature, a roughness feature, an elongation feature, a convexityfeature, an equivalent diameter feature, a radial distance feature, anarea feature, or a sphericity feature. Feature extraction logic 757 mayalso select a subset of features from the set of features. Featureextraction logic 757 may select the subset of features based on, atleast in part, a PCA-VIP ranking of the set of features. In oneembodiment, feature extraction logic also extracts a set of tortuosityfeatures from the image.

In one embodiment, feature extraction logic 757 extracts the set offeatures from a DCE-MR image during a first post-contrast phase, andduring a second, peak-contrast phase. The first post-contrast phase maybe the first scan collected following intravenous contrast agentinjection. The second, peak-contrast phase may be a scan during whichcontrast enhancement intensity is the greatest, or during which contrastenhancement intensity is within a threshold level.

Probability logic 758 computes a probability that the nodule tissue isbenign tissue. Probability logic 758 may compute the probability based,at least in part, on the subset of features. Probability logic 758 mayalso compute a probability that the nodule tissue will demonstrate aresponse to a treatment. In one embodiment, probability logic 758computes a probability that a breast cancer nodule or tumor will achievepCR after receiving NAC treatment.

Classification logic 759 classifies the nodule tissue based, at least inpart, on the set of features or the probability. In one embodiment,classification logic 759 classifies the nodule as a benign Gr or amalignant carcinoma using an SVM classifier. The SVM classifier may betrained on a set of training features using a three-fold crossvalidation re-sampling approach. The set of training features may beselected using a PCA-VIP ranking of a set of features extracted from aset of training images. The set of training images may include ano-contrast CT image of a region of tissue demonstrating lung cancerpathology, or granuloma secondary to histoplasmosis infection. The setof training images may also include a DCE-MR image of a region of tissuedemonstrating breast cancer pathology. In one embodiment, classificationlogic 759 classifies the nodule tissue as a carcinoma or a Gr using anLDA of the subset of features or using a QDA of the subset of features.In another embodiment, classification logic 759 may classify the noduletissue using other analytical techniques.

In one embodiment, classification logic 759 classifies the nodule tissueas within a threshold probability of responding to NAC. Classificationlogic 759 bases the classification, at least in part, on the probabilityor the set of features. The set of features includes at least twofeatures extracted from a DCE-MR image during the first post-contrastphase, and at least three features extracted from the DCE-MR imageduring the second, peak-contrast phase. The at least two features andthe at least three features may be selected from the set of featuresusing a feed forward feature selection approach.

In another embodiment, classification logic 759 may control a CADxsystem to classify the image based, at least in part, on theclassification. For example, classification logic 759 may control a lungcancer CADx system to classify the image based, at least in part, on theset of features. In other embodiments, other types of CADx systems maybe controlled, including CADx systems for distinguishing nodules amongbreast cancer, oral cancer, prostate cancer, colon cancer, brain cancer,and other diseases where disease classification and prognosis predictionmay be based on textural or shape features quantified from CT images ofa nodule or DCE-MR images of a region of tissue demonstrating cancerouspathology.

In one embodiment of apparatus 700, the set of logics 750 also includesa tortuosity logic. The tortuosity logic identifies a vessel associatedwith the nodule. The tortuosity logic identifies the centerline and abranching point of the vessel associated with the nodule. The tortuositylogic computes a torsion for the segment of the vessel. The tortuositylogic also computes a curvature of a voxel of a vessel segment, wherethe curvature is proportional to the inverse of an osculating circle'sradius. The tortuosity logic extracts a set of tortuosity features fromthe image. The set of tortuosity features may include the mean oftorsion of a vessel segment, or the standard deviation of torsion of avessel segment. The set of tortuosity features also may include the meanand standard deviation of the mean curvature of a group of vesselsegments. The set of tortuosity features also may include the mean andstandard deviation of the standard deviation of a vessel segmentcurvature and a total vessel segment length. The set of tortuosityfeatures may also include branching statistics associated with thevessel. The tortuosity logic also selects a subset of tortuosityfeatures from the set of tortuosity features based, at least in part, ona PCA or PCA-VIP of the set of tortuosity features. The subset oftortuosity features may include at least three tortuosity features. Inone embodiment, the classification logic 759 classifies the noduletissue based, at least in part, on the set of features, where the set offeatures includes the first set of texture features, the second set oftexture features, the set of shape features, and the set of tortuosityfeatures.

In one embodiment of apparatus 700, the set of logics 750 also includesa display logic. The display logic may control the CADx system todisplay the classification, the nodule, the perinodular zone, thetexture features, the tortuosity features, or the shape features, on acomputer monitor, a smartphone display, a tablet display, or otherdisplays. Displaying the classification or the features may also includeprinting the classification or the features. The display logic may alsocontrol the CADx to display an image of the region of tissuedemonstrating a nodule. The image of the region of tissue demonstratinga nodule may include a delineated or segmented representation of thenodule. By displaying the features and the image of the nodule, exampleapparatus provide a timely and intuitive way for a human pathologist tomore accurately classify pathologies demonstrated by a patient, thusimproving on conventional approaches to predicting cancer recurrence anddisease progression.

FIG. 8 illustrates an example computer 800 in which example methodsillustrated herein can operate and in which example circuits or logicsmay be implemented. In different examples, computer 800 may be part of aCT system or MRI system, may be operably connectable to a CT system orMRI system, or may be part of a CADx system.

Computer 800 includes a processor 802, a memory 804, and input/outputports 810 operably connected by a bus 808. In one example, computer 800may include a set of logics 830 that perform a method of characterizinga nodule in a region of lung tissue. Thus, the set of logics 830,whether implemented in computer 800 as hardware, firmware, software,and/or a combination thereof may provide means (e.g., hardware,software) for characterizing a nodule in a region of lung tissue. Indifferent examples, the set of logics 830 may be permanently and/orremovably attached to computer 800. In one embodiment, the functionalityassociated with the set of logics 830 may be performed, at least inpart, by hardware logic components including, but not limited to,field-programmable gate arrays (FPGAs), application specific integratedcircuits (ASICs), application specific standard products (ASSPs), systemon a chip systems (SOCs), or complex programmable logic devices (CPLDs).In one embodiment, individual members of the set of logics 830 areimplemented as ASICs or SOCs.

Processor 802 can be a variety of various processors including dualmicroprocessor and other multi-processor architectures. Memory 804 caninclude volatile memory and/or non-volatile memory. A disk 806 may beoperably connected to computer 800 via, for example, an input/outputinterface (e.g., card, device) 818 and an input/output port 810. Disk806 may include, but is not limited to, devices like a magnetic diskdrive, a tape drive, a Zip drive, a solid state device (SSD), a flashmemory card, or a memory stick. Furthermore, disk 806 may includeoptical drives like a CD-ROM or a digital video ROM drive (DVD ROM).Memory 804 can store processes 814 or data 817, for example. Disk 806 ormemory 804 can store an operating system that controls and allocatesresources of computer 800.

Bus 808 can be a single internal bus interconnect architecture or otherbus or mesh architectures. While a single bus is illustrated, it is tobe appreciated that computer 800 may communicate with various devices,logics, and peripherals using other busses that are not illustrated(e.g., PCIE, SATA, Infiniband, 1394, USB, Ethernet).

Computer 800 may interact with input/output devices via I/O interfaces818 and input/output ports 810. Input/output devices can include, butare not limited to, digital whole slide scanners, a CT machine, anoptical microscope, a keyboard, a microphone, a pointing and selectiondevice, cameras, video cards, displays, disk 806, network devices 820,or other devices. Input/output ports 810 can include but are not limitedto, serial ports, parallel ports, or USB ports.

Computer 800 may operate in a network environment and thus may beconnected to network devices 820 via I/O interfaces 818 or I/O ports810. Through the network devices 820, computer 800 may interact with anetwork. Through the network, computer 800 may be logically connected toremote computers. The networks with which computer 800 may interactinclude, but are not limited to, a local area network (LAN), a wide areanetwork (WAN), or other networks.

References to “one embodiment”, “an embodiment”, “one example”, and “anexample” indicate that the embodiment(s) or example(s) so described mayinclude a particular feature, structure, characteristic, property,element, or limitation, but that not every embodiment or examplenecessarily includes that particular feature, structure, characteristic,property, element or limitation. Furthermore, repeated use of the phrase“in one embodiment” does not necessarily refer to the same embodiment,though it may.

“Computer-readable storage medium”, as used herein, refers to a mediumthat stores instructions or data. “Computer-readable storage medium”does not refer to propagated signals. A computer-readable storage mediummay take forms, including, but not limited to, non-volatile media, andvolatile media. Non-volatile media may include, for example, opticaldisks, magnetic disks, tapes, and other media. Volatile media mayinclude, for example, semiconductor memories, dynamic memory, and othermedia. Common forms of a computer-readable storage medium may include,but are not limited to, a floppy disk, a flexible disk, a hard disk, amagnetic tape, other magnetic medium, an application specific integratedcircuit (ASIC), a compact disk (CD), other optical medium, a randomaccess memory (RAM), a read only memory (ROM), a memory chip or card, amemory stick, a data storage device, and other media from which acomputer, a processor or other electronic device can read.

“Logic”, as used herein, includes but is not limited to hardware,firmware, software in execution on a machine, or combinations of each toperform a function(s) or an action(s), or to cause a function or actionfrom another circuit, method, or system. Logic may include a softwarecontrolled microprocessor, a discrete logic (e.g., ASIC), an analogcircuit, a digital circuit, a programmed logic device, a memory devicecontaining instructions, and other physical devices. Logic may includeone or more gates, combinations of gates, or other circuit components.Where multiple logical logics are described, it may be possible toincorporate the multiple logics into one physical logic or circuit.Similarly, where a single logical logic is described, it may be possibleto distribute that single logic between multiple logics or circuits.

To the extent that the term “includes” or “including” is employed in thedetailed description or the claims, it is intended to be inclusive in amanner similar to the term “comprising” as that term is interpreted whenemployed as a transitional word in a claim.

Throughout this specification and the claims that follow, unless thecontext requires otherwise, the words ‘comprise’ and ‘include’ andvariations such as ‘comprising’ and ‘including’ will be understood to beterms of inclusion and not exclusion. For example, when such terms areused to refer to a stated integer or group of integers, such terms donot imply the exclusion of any other integer or group of integers.

To the extent that the term “or” is employed in the detailed descriptionor claims (e.g., A or B) it is intended to mean “A or B or both”. Whenthe applicants intend to indicate “only A or B but not both” then theterm “only A or B but not both” will be employed. Thus, use of the term“or” herein is the inclusive, and not the exclusive use. See, Bryan A.Garner, A Dictionary of Modern Legal Usage 624 (2d. Ed. 1995).

While example systems, methods, and other embodiments have beenillustrated by describing examples, and while the examples have beendescribed in considerable detail, it is not the intention of theapplicants to restrict or in any way limit the scope of the appendedclaims to such detail. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the systems, methods, and other embodiments described herein.Therefore, the invention is not limited to the specific details, therepresentative apparatus, and illustrative examples shown and described.Thus, this application is intended to embrace alterations,modifications, and variations that fall within the scope of the appendedclaims.

What is claimed is:
 1. A non-transitory computer-readable storage devicestoring computer executable instructions that when executed by acomputer control the computer to perform a method for characterizing anodule in a region of tissue, the method comprising: accessing an imageof a region of tissue demonstrating cancerous pathology; segmenting anodule in the image by extracting a nodule boundary from the image;defining a perinodular region in the image; generating a set ofperinodular texture features; computing a probability that the nodule isbenign based, at least in part, on the set of perinodular texturefeatures; and controlling a computer aided diagnosis (CADx) system togenerate a classification of the nodule based, at least in part, on theset of perinodular texture features, or the probability that the noduleis benign.
 2. The non-transitory computer-readable storage device ofclaim 1, where accessing the image of the region of tissue comprisesaccessing a computed tomography (CT) image of a region of lung tissue,where the CT image is a no-contrast chest CT image.
 3. Thenon-transitory computer-readable storage device of claim 2, wheresegmenting the nodule comprises automatically segmenting the nodule bydistinguishing nodule tissue in the image from the background of theimage using a spectral embedding gradient vector flow active contour(SEGvAC) approach.
 4. The non-transitory computer-readable storagedevice of claim 3, where distinguishing nodule tissue in the image fromthe background of the image using the SEGvAC approach comprises:generating an initial lung mask by separating a lung region representedin the image from surrounding lung anatomy; generating a refined imageby removing a non-granuloma structure from the initial lung mask using arule-based classifier, where the rule-based classifier selects thenon-granuloma structure to remove from the initial lung mask based on aconvexity measure of the non-granuloma structure, or an elongationmeasure of the non-granuloma structure; generating a spectral embedding(SE) representation by projecting at least one refined image into athree dimensional (3D) SE space; and extracting a nodule boundary fromthe SE representation using a gradient vector flow field (GVF) activecontour model, where a GVF force drives the active contour.
 5. Thenon-transitory computer-readable storage device of claim 4, wheregenerating the initial lung mask further comprises refining the initiallung mask by applying morphological hole-filling to a logical complementof the initial lung mask.
 6. The non-transitory computer-readablestorage device of claim 4, where extracting the nodule boundary from theSE representation comprises calculating a tensor gradient functionderived from the SE representation.
 7. The non-transitorycomputer-readable storage device of claim 4, where a GVF force iscalculated based on a generalized diffusion equation applied to acomponent of an image edge map of the CT image of the region of lungtissue.
 8. The non-transitory computer-readable storage device of claim1, where defining the perinodular region comprises: generating an outerperinodular boundary by dilating the nodule boundary a threshold amount;generating a perinodular region by subtracting the nodule from theregion defined by the outer perinodular boundary.
 9. The non-transitorycomputer-readable storage device of claim 1, where generating the set ofperinodular texture features comprises: extracting a first set oftexture features from the perinodular region; and reducing the first setof texture features using principal component analysis (PCA) variableimportance on projections (VIP) feature ranking.
 10. The non-transitorycomputer-readable storage device of claim 1, the method furthercomprising: extracting a set of nodule features from the nodule, wherethe set of nodule features comprises a set of shape features or a secondset of texture features; generating a reduced set of nodule features byreducing the set of nodule features using PCA-VIP feature ranking. 11.The non-transitory computer-readable storage device of claim 10, wherethe set of perinodular texture features or the second set of texturefeatures includes a gray-level statistical feature, a steerable Gaborfeature, a Haralick feature, a Law feature, a Law-Laplacian feature, alocal binary pattern (LBP) feature, an inertia feature, a correlationfeature, a difference entropy feature, a contrast inverse momentfeature, a gradient feature, a co-occurrence of local anisotropicgradient orientations (CoLIAGe) feature, or a contrast variance feature.12. The non-transitory computer-readable storage device of claim 10,where the set of shape features includes a size feature, an areafeature, a perimeter feature, an eccentricity feature, an extendfeature, a compactness feature, a radial distance feature, a roughnessfeature, an elongation feature, a convexity feature, an equivalentdiameter feature, or a sphericity feature.
 13. The non-transitorycomputer-readable storage device of claim 10, where the CADx systemgenerates the classification of the image of the nodule using a supportvector machine (SVM) classifier, where the SVM classifier classifies theimage of the nodule with an accuracy of at least 0.77 area under thecurve (AUC).
 14. The non-transitory computer-readable storage device ofclaim 13, where the SVM classifier is trained using the set ofperinodular texture features or the reduced set of nodule features witha radial-based kernel (RBF) function.
 15. The non-transitorycomputer-readable storage device of claim 10, where generating theclassification of the nodule is based, at least in part, on the set ofperinodular texture features, the reduced set of nodule features, or theprobability that the nodule is a benign Gr.
 16. The non-transitorycomputer-readable storage device of claim 1, where accessing the imageof the region of tissue comprises accessing a dynamic contrast-enhanced(DCE) magnetic resonance imaging (MRI) image of a region of breasttissue.
 17. The non-transitory computer-readable storage device of claim16, where defining the perinodular region comprises: segmenting a breastwall from the image; removing the breast wall from the image; generatingan outer perinodular boundary by dilating the nodule boundary athreshold amount; generating a perinodular region by subtracting thenodule from the region defined by the outer perinodular boundary. 18.The non-transitory computer-readable storage device of claim 17, themethod further comprising computing a probability that the nodule willachieve pathological complete response (pCR) based, at least in part, onthe set of perinodular texture features; and controlling the CADx systemto generate a classification of the nodule based, at least in part, onthe set of perinodular texture features, or the probability that thenodule will achieve pCR.
 19. An apparatus for classifying a region oftissue in an image, comprising: a processor; a memory; an input/outputinterface; a set of logics; and an interface to connect the processor,the memory, the input/output interface and the set of logics, where theset of logics includes: an image acquisition logic that acquires animage of a region of tissue demonstrating cancerous pathology; adelineation logic that distinguishes nodule tissue in the image from thebackground of the image by defining a nodule boundary; a perinodularzone logic that defines a perinodular zone based, at least in part, onthe nodule tissue or the nodule boundary; a feature extraction logicthat extracts a set of features from the image, where the set offeatures comprises a first set of texture features extracted from thenodule tissue in the image or a set of shape features extracted from thenodule tissue in the image, and a second set of texture featuresextracted from the perinodular region; a probability logic that computesa probability that the nodule tissue is benign tissue, or that thenodule tissue will demonstrate a response to a treatment; and aclassification logic that classifies the nodule tissue based, at leastin part, on the set of features, or the probability.
 20. The apparatusof claim 19, where the image is a no-contrast computed tomography imageof a region of tissue demonstrating lung cancer pathology, or where theimage is dynamic contrast enhanced (DCE) magnetic resonance (MR) imageof a region of tissue demonstrating breast cancer pathology.
 21. Theapparatus of claim 19, where the first set of texture features or thesecond set of texture features includes a gray-level statisticalfeature, a steerable Gabor feature, a Haralick feature, a Law feature, aLaw-Laplacian feature, a gradient feature, a local binary pattern (LBP)feature, an inertia feature, a correlation feature, a difference entropyfeature, a contrast inverse moment feature, a co-occurrence of localanisotropic gradient orientations (CoLIAGe) feature, or a contrastvariance feature, where the set of shape features includes a sizefeature, an area feature, a perimeter feature, an eccentricity feature,an extend feature, a compactness feature, a radial distance feature, aroughness feature, an elongation feature, a convexity feature, anequivalent diameter feature, or a sphericity feature, and where thefeature extraction logic selects a subset of features from the set offeatures based on, at least in part, a principal component analysis(PCA) variable importance on projections (VIP) ranking of the set offeatures.
 22. The apparatus of claim 19, where the delineation logicdistinguishes nodule tissue in the image from the background of theimage using a spectral embedding gradient vector flow active contour(SEGvAC) approach.
 23. The apparatus of claim 19, where the perinodularzone logic defines the perinodular zone by defining a perinodularzone-plus-nodule region by dilating the nodule boundary a thresholdamount, and by subtracting the nodule region from the perinodularzone-plus-nodule region.
 24. The apparatus of claim 19, where theprobability logic computes the probability that the nodule tissue willdemonstrate a response to a treatment by computing the probability thatthe nodule will achieve pathological complete response (pCR) toneo-adjuvant chemotherapy (NAC).
 25. The apparatus of claim 19, wherethe classification logic classifies the nodule tissue as a benigngranuloma or a malignant carcinoma using a support vector machine (SVM)classifier, where the SVM classifier is trained on a set of trainingfeatures using a three-fold cross-validation re-sampling approach, andwhere the set of training features is selected using a PCA-VIP rankingof a set of features extracted from a set of training images.
 26. Theapparatus of claim 24, where the classification logic classifies thenodule tissue as within a threshold probability of responding to NACbased on the probability or the set of features, where the set offeatures includes at least two features extracted during a firstpost-contrast phase, and at least three features extracted during asecond, peak-contrast phase.
 27. A method for distinguishing benignnodules from cancerous tumors in a medical image, the method comprising:accessing a radiological image of a region of tissue demonstrating lungnodules; segmenting a nodule in the image from the background of theimage; defining a perinodular region based, at least in part, on thenodule; selecting a first set of texture features from perinodularregion; selecting a second set of texture features or a set of shapefeatures from the nodule; generating a probability that the nodule isbenign based, at least in part, on the first set of texture features,the second set of texture features, or the set of shape features; andproviding a prognosis prediction based on the probability.